Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness

Abstract

Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation, and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints, data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainable AI, and co-creation of health and wellness interventions.

Disclosure statement

There are no financial or non-financial competing interests to report.

Additional information

Funding

This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program. We acknowledge the support of Mitacs Canada and the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Discovery Grant.

Notes on contributors

Oladapo Oyebode

Oladapo Oyebode is a Ph.D. candidate at Dalhousie University. His research interests include Human-Computer Interaction, Persuasive Technology, Adaptive Systems, Affective Computing, Artificial Intelligence, Digital Health, and Health Informatics. He has published over 30 peer-reviewed research articles. He is currently designing and developing technologies to tackle health-related (including mental health) issues.

Jonathon Fowles

Jonathon Fowles is the professor with the School of Kinesiology and Director of the Centre of Lifestyle Studies at Acadia University, Chair of the National Advisory Council for Exercise is Medicine Canada, and Fellow of the Canadian Society for Exercise Physiology. He believes technology can enhance health and wellness.

Darren Steeves

Darren Steeves is an Adjunct Professor at the School of Health and Human Performance, Dalhousie University. His passion is to help people through living his values of collaboration, innovation, curiosity, and integrity. He has 25-year background in health and performance, consulting with top executives, Olympic medalists, and world champion athletes.

Rita Orji

Rita Orji is a Canada Research Chair in Persuasive Technology and a Computer Science Professor at Dalhousie University where she directs Persuasive Computing Lab. Her research is at the intersection of technology and human behaviour, focusing on investigating user-centered approaches to designing technologies that improve lives and promote desirable behaviours.